Multi-view aggregation. Tab. REF  shows performances of different multi-view aggregation settings. We first adopt average aggregation and test different aggregation stages. The positional encoding is just a linear mapping with LayerNorm [1]}. If we aggregate after the positional encoding generation (\(\rm {PE}\) ), it is equivalent to averaging the coordinates of multiple views. It performs badly since the position information is destroyed. To aggregate after object feature generation (\(+x\) ), we first pass the object features into a 2-layers transformer encoder [2]} and then fuse object features from different views. It keeps the position information but still can not achieve the best performance. Aggregation after multi-modal feature fusion (\(*L\) ) achieves the best performance. We also test different aggregation functions. “Avg” is more effective than “Max”, and “Avg + Max” achieves similar performance as “Avg”.
